345 datasets found

The COCO dataset is a large-scale dataset for object detection, semantic segmentation, and captioning. It contains 80 object categories and 1,000 image instances per category,...

The FSS-1000 dataset contains 1000 images with 1000 object categories.

The dataset used in the paper is PASCAL VOC2007 test dataset and VOC2012 trainval datasets.

Tiny Images dataset is a large dataset of images, each with a small size.

Large-scale vision-and-language models trained on curated and web-scrapped data have led to significant improvements over task-specific models when transferred to downstream...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

The COCO2017 dataset is used for the trainability experiment, which includes 47,429 images with 210,893 objects.

The COCO-Thing-Stuff dataset is used for the L2I task, which includes 118,287 training images and 5,000 validation images. Each image is annotated with bounding boxes and...

The CornerNet dataset is a single-stage object detection benchmark.

Grid R-CNN is a novel object detection framework that adopts a grid guided localisation mechanism for accurate object detection.

Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

The COCO-QA dataset is used for visual question answering task. It consists of 123,287 images and 78,736 train and 38,948 test questions.

Instance segmentation (IS) is an important computer vision task, aiming at simultaneously predicting the class label and the binary mask for each instance of interest in an image.

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...

The Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and...

The ShanghaiTech dataset includes 330 training and 107 test videos recorded at 13 different background locations.

A benchmark for low-shot recognition was proposed by Hariharan & Girshick (2017) and consists of a representation learning phase without access to the low-shot classes and a...

The DOTA dataset is a large aerial image dataset for oriented objects detection which contains 2806 images with the size ranges from 800 × 800 to 4000 × 4000 and 188282...